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Hyperspectral Feature Extraction Based On The Reference Spectral Background Removal Method

Hengqian Zhao, Lifu Zhang, Xia Zhang, Jia Liu, Taixia Wu, Shudong Wang
2015 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this paper, a new spectral feature extraction method named reference spectral background removal (RSBR) is proposed.  ...  Index Terms-Diagnostic absorption feature, hyperspectral remote sensing, mineralogy, reference spectral background removal (RSBR).  ...  In this paper, a new method named reference spectral background removal (RSBR) is proposed.  ... 
doi:10.1109/jstars.2015.2401052 fatcat:6p6ld7auefdildtefhsawa32dq

Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

Dandan Ma, Yuan Yuan, Qi Wang
2019 Remote Sensing  
First, as spatial structure is beneficial for recognizing target, a new spectral–spatial feature extraction technique is used in this work based on the PCA technique and edge-preserving filtering.  ...  To address this problem, this paper proposes a novel hyperspectral anomaly detection method based on separability-aware sample cascade model.  ...  Author Contributions: All authors contributed to proposing the method, carrying out the experiments and analyzing the results.  ... 
doi:10.3390/rs11212537 fatcat:pow6ajpusfckjl3g4lj2uwekty

SHIP DETECTION BASED ON MULTIPLE FEATURES IN RANDOM FOREST MODEL FOR HYPERSPECTRAL IMAGES

N. Li, L. Ding, H. Zhao, J. Shi, D. Wang, X. Gong
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features.  ...  Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images.  ...  Spatial and Spectral Features Extraction In order to improve the performance of ship detection methods, feature extraction methods are introduced to hyperspectral images to obtain spectral and texture  ... 
doi:10.5194/isprs-archives-xlii-3-891-2018 fatcat:2qkzy6zxhzhtrilfi6xol3d44y

Visual Attention and Background Subtraction with Adaptive Weight for Hyperspectral Anomaly Detection

Pei Xiang, Jiangluqi Song, Hanlin Qin, Wei Tan, Huan Li, Huixin Zhou
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Then, the hyperspectral visual attention model is introduced, for the first time, into hyperspectral AD for extracting the salient feature map of the input images.  ...  In the experiment, the proposed method is compared with seven other state-of-the-art methods on synthetic and real-world HSI.  ...  ACKNOWLEDGMENT The authors would like to thank all the developers of all alternative methods for providing their code and datasets.  ... 
doi:10.1109/jstars.2021.3052968 fatcat:j3ltmp3gpbbhrpq6kvcpvmg67i

A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

Pouria Sadeghi-Tehran, Nicolas Virlet, Malcolm J. Hawkesford
2021 Remote Sensing  
The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA  ...  Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies.  ...  Funding: Rothamsted Research receives grant-aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) and the project was directly funded by the Designing Future Wheat strategic  ... 
doi:10.3390/rs13050898 fatcat:henfwag4d5gifalkm62dxpkloq

A Rapid Diagnostic Grading System for Cucumber Downy Mildew Based on Visible Light - Hyperspectral Imaging System

Chunyang Yao, Xiaodong Zhang, Hanping Mao, Hongyan Gao, Qinglin Li
2020 JOURNAL OF ADVANCES IN AGRICULTURE  
In addition, the stepwise regression method and PCA were used to reduce and extract the feature information of sensitive bands.  ...  The model based on the stepwise regression method is used to classify and identify downy mildew and normal leaves.  ...  Acknowledgements Chunyang yao is Student of the Agricultural Engineering College of Jiangsu University, mainly engaged in the analysis of spectrum and imaging of pests and diseases of facility crops.  ... 
doi:10.24297/jaa.v11i.8779 fatcat:xywkwq5axncbjilbcldhym2juu

ALTERATION MINERALS EXTRACTION USING AIRBORNE HYPERSPECTRAL DATA CASI AND SASI IN WUYI METALLOGENIC BELT, CHINA

Z. Huang, J. Zheng
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The alteration minerals extraction based on integrated CASI_SASI reflectance data were processed by MTMF algorithm with the input imagery which was pre-processed by MNF and the input endmember spectra  ...  The objective of research is to mapping the alteration minerals for mineral exploration using mixture tuned matched filtering (MTMF) approach based on airborne hyperspectral data CASI and SASI in Wuyi  ...  ACKNOWLEDGEMENTS (OPTIONAL) This work is supported by the National Key R&D Program of China (2016YFC0600210) and the National Natural Science Foundation of China (41272366).  ... 
doi:10.5194/isprs-archives-xlii-3-601-2018 fatcat:37vr5mjs7fcirkpxw2kilxaeje

Foreword to the Special Issue on Hyperspectral Image and Signal Processing

Jocelyn Chanussot, Melba M. Crawford, Bor-Chen Kuo
2010 IEEE Transactions on Geoscience and Remote Sensing  
Foreword to the special issue on hyperspectral image and signal processing.  ...  Be they unsupervised (clustering) or supervised (algorithms based on training and machine learning), most of the published methods are explicitly or implicitly based on statistical modeling of the spectral  ...  These methods, which are referred to as spectral-spatial classifiers, typically focus on local spatial information and are particularly successful for data with large homogeneous regions or where spectral  ... 
doi:10.1109/tgrs.2010.2085313 fatcat:dwy3ouhyxrg6bnu6hmezsf75se

Foreword to the Special Issue on Hyperspectral Image and Signal Processing

Devis Tuia, Sebastian Lopez, Michael Schaepman, Jocelyn Chanussot
2015 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Foreword to the special issue on hyperspectral image and signal processing.  ...  Be they unsupervised (clustering) or supervised (algorithms based on training and machine learning), most of the published methods are explicitly or implicitly based on statistical modeling of the spectral  ...  These methods, which are referred to as spectral-spatial classifiers, typically focus on local spatial information and are particularly successful for data with large homogeneous regions or where spectral  ... 
doi:10.1109/jstars.2015.2452732 fatcat:pffxpzv6d5bipknaod46prsnnm

Random Collective Representation-Based Detector with Multiple Features for Hyperspectral Images

Zhongheng Li, Fang He, Haojie Hu, Fei Wang, Weizhong Yu
2021 Remote Sensing  
This method first extract the different features include spectral feature, Gabor feature, extended multiattribute profile (EMAP) feature, and extended morphological profile (EMP) feature matrix from the  ...  Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD).  ...  Acknowledgments: We thank the editors and reviewers for their insightful comments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13040721 fatcat:ieyncddqkbai3kllhpvvvl6qim

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

Tian Gao, Anil Kumar Nalini Chandran, Puneet Paul, Harkamal Walia, Hongfeng Yu
2021 Sensors  
The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation.  ...  The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species.  ...  Acknowledgments: The authors would like to thank the staff members of the University of Nebraska-Lincoln's Plant Pathology Greenhouse for their help in the data collection.  ... 
doi:10.3390/s21248184 pmid:34960287 pmcid:PMC8703337 fatcat:dwpkjfn27fclzkonuqaxmkgk7i

Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation

Dandan Ma, Yuan Yuan, Qi Wang
2018 Remote Sensing  
Firstly, a new spectral feature selection framework based on sparse presentation is designed, which is closely guided by the anomaly detection task.  ...  The proposed method is compared with ten state-of-the-art methods including LRX, SRD, CRD, LSMAD, RSAD, BACON, BACON-target, GRX, GKRX, and PCA-GRX on three real-world hyperspectral images.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs10050745 fatcat:4fvblawi3fg2bpci36kzrkapei

Tensor Representation and Manifold Learning Methods for Remote Sensing Images [article]

Lefei Zhang
2014 arXiv   pre-print
More precisely, we adopt the manifold learning algorithms as the mainline and unify the regularization theory, tensor-based method, sparse learning and transfer learning into the same framework.  ...  One of the main purposes of earth observation is to extract interested information and knowledge from remote sensing (RS) images with high efficiency and accuracy.  ...  The observed spectral feature of the desired target pixel (positive sample) is therefore a mixed signature of the reference target spectrum and the background pixels spectra (negative samples), which belong  ... 
arXiv:1401.2871v1 fatcat:7riwgc3pc5hcpm3iczsy2tsali

Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery

Baohua Yang, Jifeng Ma, Xia Yao, Weixing Cao, Yan Zhu
2021 Sensors  
The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect.  ...  Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features.  ...  We are grateful to the reviewers for their suggestions and comments, which significantly improved the quality of this paper. Conflicts of Interest: All the authors declare no conflict of interest.  ... 
doi:10.3390/s21020613 pmid:33477350 fatcat:bhci5clrg5apnmv5lkkspbgboe

Background Information Self-Learning Based Hyperspectral Target Detection

Yufei Tian, Jihai Yang, Shijun Li, Wenning Xu
2018 Complexity  
In this paper we proposed a target detection method based on background self-learning to extract the biologic information from the hyperspectral images.  ...  The experimental results show the validity and the superiority of our method on detecting the biologic information from hyperspectral images.  ...  Acknowledgments This work is supported by the CRSRI Open Research Program (Program no. CKWV2016380/KY).  ... 
doi:10.1155/2018/3502508 fatcat:z5vc6tioavc7npqnqskpssfhqi
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